Merge branch 'fieldsIO' into neuralpint

.github/workflows/ci_pipeline.yml

@@ -55,6 +55,9 @@ jobs:
       - name: Install additional packages as needed
         run: |
           micromamba install -y --file etc/environment-tests.yml --freeze-installed
+      - name: Install pySDC as a package in the current environment
+        run: |
+          pip install --no-deps -e .
       - name: Run pytest for CPU stuff
         run: |
           echo "print('Loading sitecustomize.py...')

.gitignore

@@ -10,6 +10,7 @@ step_*.png
 *.swp
 *_data.json
 !_dataRef.json
+*.pysdc
 
 # Created by https://www.gitignore.io
 

pySDC/helpers/blocks.py

@@ -0,0 +1,138 @@
+class BlockDecomposition(object):
+    """
+    Class decomposing a cartesian space domain (1D to 3D) into a given number of processors.
+
+    Parameters
+    ----------
+    nProcs : int
+        Total number of processors for space block decomposition.
+    gridSizes : list[int]
+        Number of grid points in each dimension
+    algo : str, optional
+        Algorithm used for hte block decomposition :
+
+        - Hybrid : approach minimizing interface communication, inspired from
+          the `[Hybrid CFD solver] <https://web.stanford.edu/group/ctr/ResBriefs07/5_larsson1_pp47_58.pdf>`_.
+        - ChatGPT : quickly generated using `[ChatGPT] <https://chatgpt.com>`_.
+        The default is "Hybrid".
+    gRank : int, optional
+        If provided, the global rank that will determine the local block distribution. Default is None.
+    order : str, optional
+        The order used when computing the rank block distribution. Default is `C`.
+    """
+
+    def __init__(self, nProcs, gridSizes, algo="Hybrid", gRank=None, order="C"):
+        dim = len(gridSizes)
+        assert dim in [1, 2, 3], "block decomposition only works for 1D, 2D or 3D domains"
+
+        if algo == "ChatGPT":
+
+            nBlocks = [1] * dim
+            for i in range(2, int(nProcs**0.5) + 1):
+                while nProcs % i == 0:
+                    nBlocks[0] *= i
+                    nProcs //= i
+                    nBlocks.sort()
+
+            if nProcs > 1:
+                nBlocks[0] *= nProcs
+
+            nBlocks.sort()
+            while len(nBlocks) < dim:
+                smallest = nBlocks.pop(0)
+                nBlocks += [1, smallest]
+                nBlocks.sort()
+
+            while len(nBlocks) > dim:
+                smallest = nBlocks.pop(0)
+                next_smallest = nBlocks.pop(0)
+                nBlocks.append(smallest * next_smallest)
+                nBlocks.sort()
+
+        elif algo == "Hybrid":
+            rest = nProcs
+            facs = {
+                1: [1],
+                2: [2, 1],
+                3: [2, 3, 1],
+            }[dim]
+            exps = [0] * dim
+            for n in range(dim - 1):
+                while (rest % facs[n]) == 0:
+                    exps[n] = exps[n] + 1
+                    rest = rest // facs[n]
+            if rest > 1:
+                facs[dim - 1] = rest
+                exps[dim - 1] = 1
+
+            nBlocks = [1] * dim
+            for n in range(dim - 1, -1, -1):
+                while exps[n] > 0:
+                    dummymax = -1
+                    dmax = 0
+                    for d, nPts in enumerate(gridSizes):
+                        dummy = (nPts + nBlocks[d] - 1) // nBlocks[d]
+                        if dummy >= dummymax:
+                            dummymax = dummy
+                            dmax = d
+                    nBlocks[dmax] = nBlocks[dmax] * facs[n]
+                    exps[n] = exps[n] - 1
+
+        else:
+            raise NotImplementedError(f"algo={algo}")
+
+        # Store attributes
+        self.dim = dim
+        self.nBlocks = nBlocks
+        self.gridSizes = gridSizes
+
+        # Used for rank block distribution
+        self.gRank = gRank
+        self.order = order
+
+    @property
+    def ranks(self):
+        gRank, order = self.gRank, self.order
+        assert gRank is not None, "gRank attribute need to be set"
+        dim, nBlocks = self.dim, self.nBlocks
+        if dim == 1:
+            return (gRank,)
+        elif dim == 2:
+            div = nBlocks[-1] if order == "C" else nBlocks[0]
+            return (gRank // div, gRank % div)
+        else:
+            raise NotImplementedError(f"dim={dim}")
+
+    @property
+    def localBounds(self):
+        iLocList, nLocList = [], []
+        for rank, nPoints, nBlocks in zip(self.ranks, self.gridSizes, self.nBlocks):
+            n0 = nPoints // nBlocks
+            nRest = nPoints - nBlocks * n0
+            nLoc = n0 + 1 * (rank < nRest)
+            iLoc = rank * n0 + nRest * (rank >= nRest) + rank * (rank < nRest)
+
+            iLocList.append(iLoc)
+            nLocList.append(nLoc)
+        return iLocList, nLocList
+
+
+if __name__ == "__main__":
+    # Base usage of this module for a 2D decomposition
+    from mpi4py import MPI
+    from time import sleep
+
+    comm: MPI.Intracomm = MPI.COMM_WORLD
+    MPI_SIZE = comm.Get_size()
+    MPI_RANK = comm.Get_rank()
+
+    blocks = BlockDecomposition(MPI_SIZE, [256, 64], gRank=MPI_RANK)
+    if MPI_RANK == 0:
+        print(f"nBlocks : {blocks.nBlocks}")
+
+    ranks = blocks.ranks
+    bounds = blocks.localBounds
+
+    comm.Barrier()
+    sleep(0.01 * MPI_RANK)
+    print(f"[Rank {MPI_RANK}] pRankX={ranks}, bounds={bounds}")